Only about half of all machine learning models actually get deployed. Far fewer produce real value. Many early adopters have invested millions, but remain unimpressed and discouraged by the lack of returns thus far. For most organizations, getting “out of the lab” and into “production” is too hard and costly.
Despite the challenges, the community has identified the problematic areas in the process, and a solution is emerging to getting through last mile AI challenges to scale to the enterprise: ModelOps.
ModelOps platforms manage the last mile delivery challenges associated with deploying, managing, and monitoring AI models into production systems. This allows data science teams to quickly deploy AI models into production, and for development teams to easily embed these models into software applications to quickly turn any tool into one that’s powered by AI. Once deployed, these tools automate the monitoring and management part required to address changes in performance throughout model lifecycle, ensuring that model drift is detected and models can be retrained when needed.
They also allow enterprise IT departments to monitor AI usage and performance, particularly infrastructure usage. Most importantly, business leaders are brought into the conversation. ModelOps tools provide transparency, explainability, and other views that present AI information in terms understandable to business stakeholders, both improving trust in the technology, and driving further AI adoption.
This last piece missing in the puzzle is widespread AI adoption. The point of AI is to drive business value with better data-driven, decision-making with faster speed to insight, resource efficiencies and optimizations. However, we are nowhere near peak adoption. This stems from issues in the process (or lack thereof) for managing AI models in use in AI-enabled systems. Fortunately, ModelOps tools are already changing the game in enterprise AI — reducing the time it takes models into production from months to minutes. Key stakeholders across IT and business units are now brought together to pave the path forward for widespread AI adoption and business value in the future.
Pains in the Current Process
Until recently, no easy way existed to get AI models into production at the scale required for enterprise software applications. According to Gartner research, it can take data scientists up to nine months to build and deploy a machine learning or AI model into production. Next comes the handoff to a software development team, a process that today is full of friction to get the AI model to work in a production environment at scale, and embedded in a front-end application that will provide an analyst any semblance of value.
The next issue stems from the fact that many teams building AI models and applications are operating in survival mode. They’ll do whatever is necessary to get models to work. This approach is the cause of nightmares for enterprise IT leaders and program managers. Without a standardized process for building, deploying, and monitoring these systems, there’s no transparency or trust in how systems are being built. This Ultimately, AI adoption is hampered.
The most scarring effect for many organizations, is the negative impact on the business value. Organizations aren’t seeing the return of better data-driven decision-making, cost efficiencies, resource savings, or any of the other values AI promises to deliver. The lack of transparency and trust in how the technology is being developed and used only serves as more fodder for the trope that AI is too risky and not worth the investment.
ModelOps: A New Way
Although this might seem like a bleak picture, the future is bright because of ModelOps. Overcoming these obstacles will be the key to unlocking the success of deploying AI at scale for the last mile. AI is no different than any other previously emerging technology, and we need to apply lessons learned from past experiences.
- Recognize what your organization is trying to accomplish is hard, but possible. Empower your data science and development teams with the right tools that address their pain points.
- You’ll need to take a mixed approach when it comes to building vs. buying components for your AI tech stack. There are many strong, open architecture, ModelOps tool solutions that integrate easily with data scientists’ existing model training tools — allowing your development teams to continue working with their favorite languages and tools to build AI-powered applications.
The last process step will change, define, and drive AI adoption for the future. Getting stakeholders to trust and take ownership in the technology requires them understanding what’s going on under the hood. Again, not crazy science, but it’s what’s been missing from today’s approach to AI adoption. By bringing stakeholders into the conversation, organizations can establish processes for monitoring and governing AI-enabled systems the same as they would for any other IT system. Most importantly, establishing this foundation will shift focus away from the magic behind the scenes and onto realizing returns and business value. The sooner we stop banging our heads against the wall with the minutiae of how we should be building the systems, the sooner we’ll experience widespread AI adoption. The future of AI is bright. While it starts with ModelOps, it comes full circle with building business understanding, trust, and belief in the technology. Moving to the future requires taking the first steps today.
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